Overview

Dataset statistics

Number of variables10
Number of observations12124
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory947.3 KiB
Average record size in memory80.0 B

Variable types

TimeSeries5
Numeric5

Alerts

tmin is highly overall correlated with tmax and 2 other fieldsHigh correlation
tmax is highly overall correlated with tmin and 2 other fieldsHigh correlation
tmed is highly overall correlated with tmin and 2 other fieldsHigh correlation
presMin is highly overall correlated with presMaxHigh correlation
presMax is highly overall correlated with tmin and 1 other fieldsHigh correlation
velmedia is highly overall correlated with rachaHigh correlation
racha is highly overall correlated with velmediaHigh correlation
sol is highly overall correlated with tmax and 2 other fieldsHigh correlation
prec is highly overall correlated with solHigh correlation
tmin is non stationaryNon stationary
tmax is non stationaryNon stationary
tmed is non stationaryNon stationary
presMin is non stationaryNon stationary
presMax is non stationaryNon stationary
tmin is seasonalSeasonal
tmax is seasonalSeasonal
tmed is seasonalSeasonal
presMin is seasonalSeasonal
presMax is seasonalSeasonal
sol has 368 (3.0%) zerosZeros
prec has 9526 (78.6%) zerosZeros

Reproduction

Analysis started2023-03-16 13:07:52.964967
Analysis finished2023-03-16 13:08:11.491239
Duration18.53 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

tmin
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct310
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.31758
Minimum-4
Maximum29.3
Zeros11
Zeros (%)0.1%
Memory size94.8 KiB
2023-03-16T14:08:11.571017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile3.7
Q18.6
median13.4
Q318.4
95-th percentile22.4
Maximum29.3
Range33.3
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation5.9095193
Coefficient of variation (CV)0.44373822
Kurtosis-0.89451573
Mean13.31758
Median Absolute Deviation (MAD)4.9
Skewness-0.094770993
Sum161462.34
Variance34.922418
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.395188252 × 10-12
2023-03-16T14:08:11.733589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 119
 
1.0%
12 111
 
0.9%
19 107
 
0.9%
8 104
 
0.9%
10 103
 
0.8%
18.4 102
 
0.8%
10.4 101
 
0.8%
18 101
 
0.8%
10.2 100
 
0.8%
14 99
 
0.8%
Other values (300) 11077
91.4%
ValueCountFrequency (%)
-4 1
 
< 0.1%
-3.5 1
 
< 0.1%
-2.6 1
 
< 0.1%
-2.5 1
 
< 0.1%
-2 3
< 0.1%
-1.8 1
 
< 0.1%
-1.7 1
 
< 0.1%
-1.5 4
< 0.1%
-1.4 3
< 0.1%
-1.3 2
< 0.1%
ValueCountFrequency (%)
29.3 1
 
< 0.1%
28.3 1
 
< 0.1%
28.1 1
 
< 0.1%
27.8 2
< 0.1%
27.1 1
 
< 0.1%
27 2
< 0.1%
26.8 3
< 0.1%
26.6 2
< 0.1%
26.5 2
< 0.1%
26.4 4
< 0.1%
2023-03-16T14:08:12.726005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

tmax
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct385
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.745841
Minimum4
Maximum46.6
Zeros0
Zeros (%)0.0%
Memory size94.8 KiB
2023-03-16T14:08:12.984127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile14.7
Q118.9
median25
Q332.4
95-th percentile38.7
Maximum46.6
Range42.6
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation7.8598726
Coefficient of variation (CV)0.30528708
Kurtosis-1.0435284
Mean25.745841
Median Absolute Deviation (MAD)6.6
Skewness0.22470248
Sum312142.58
Variance61.777597
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.45116366 × 10-13
2023-03-16T14:08:13.131762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 116
 
1.0%
17 113
 
0.9%
19 111
 
0.9%
16 103
 
0.8%
21 96
 
0.8%
18.5 94
 
0.8%
20.5 90
 
0.7%
18.2 87
 
0.7%
20 85
 
0.7%
22 84
 
0.7%
Other values (375) 11145
91.9%
ValueCountFrequency (%)
4 1
< 0.1%
6.7 1
< 0.1%
7.2 1
< 0.1%
7.6 1
< 0.1%
7.8 1
< 0.1%
7.9 1
< 0.1%
8 1
< 0.1%
8.3 2
< 0.1%
8.6 2
< 0.1%
8.7 1
< 0.1%
ValueCountFrequency (%)
46.6 1
 
< 0.1%
45.9 1
 
< 0.1%
45.6 1
 
< 0.1%
45.2 1
 
< 0.1%
44.9 1
 
< 0.1%
44.8 3
< 0.1%
44.5 2
< 0.1%
44.4 2
< 0.1%
44.3 2
< 0.1%
44.2 2
< 0.1%
2023-03-16T14:08:13.974486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

tmed
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct330
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.532211
Minimum2.7
Maximum36.8
Zeros0
Zeros (%)0.0%
Memory size94.8 KiB
2023-03-16T14:08:14.238789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile9.7
Q113.9
median19
Q325.3
95-th percentile30.2
Maximum36.8
Range34.1
Interquartile range (IQR)11.4

Descriptive statistics

Standard deviation6.6786164
Coefficient of variation (CV)0.34192833
Kurtosis-1.0623618
Mean19.532211
Median Absolute Deviation (MAD)5.6
Skewness0.1310574
Sum236808.53
Variance44.603917
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.154826175 × 10-14
2023-03-16T14:08:14.389388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 118
 
1.0%
13.8 107
 
0.9%
12.8 107
 
0.9%
15.2 106
 
0.9%
14.8 102
 
0.8%
14 102
 
0.8%
16 101
 
0.8%
13.4 100
 
0.8%
14.4 97
 
0.8%
15 97
 
0.8%
Other values (320) 11087
91.4%
ValueCountFrequency (%)
2.7 1
 
< 0.1%
3.4 1
 
< 0.1%
3.6 1
 
< 0.1%
4 1
 
< 0.1%
4.2 1
 
< 0.1%
4.8 2
 
< 0.1%
5 3
< 0.1%
5.2 2
 
< 0.1%
5.4 7
0.1%
5.5 3
< 0.1%
ValueCountFrequency (%)
36.8 1
< 0.1%
36.3 1
< 0.1%
35.7 1
< 0.1%
35.5 1
< 0.1%
35.2 1
< 0.1%
35.1 2
< 0.1%
35 2
< 0.1%
34.9 2
< 0.1%
34.8 2
< 0.1%
34.6 2
< 0.1%
2023-03-16T14:08:15.259986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

presMin
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct477
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1011.6821
Minimum976.3
Maximum1035.4
Zeros0
Zeros (%)0.0%
Memory size94.8 KiB
2023-03-16T14:08:15.516876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum976.3
5-th percentile1003
Q11008.3
median1010.9
Q31014.9
95-th percentile1022.5
Maximum1035.4
Range59.1
Interquartile range (IQR)6.6

Descriptive statistics

Standard deviation5.9722041
Coefficient of variation (CV)0.0059032419
Kurtosis1.2703298
Mean1011.6821
Median Absolute Deviation (MAD)3.1
Skewness0.035892555
Sum12265634
Variance35.667222
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.896634085 × 10-25
2023-03-16T14:08:15.669470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1009.8 161
 
1.3%
1009 151
 
1.2%
1011.7 151
 
1.2%
1011.3 145
 
1.2%
1010.9 145
 
1.2%
1010.1 142
 
1.2%
1009.7 140
 
1.2%
1008.9 140
 
1.2%
1009.5 138
 
1.1%
1010.2 138
 
1.1%
Other values (467) 10673
88.0%
ValueCountFrequency (%)
976.3 1
< 0.1%
977.6 1
< 0.1%
982.7 1
< 0.1%
983.2 1
< 0.1%
983.3 1
< 0.1%
983.8 1
< 0.1%
984.8 2
< 0.1%
985 2
< 0.1%
985.8 2
< 0.1%
985.9 1
< 0.1%
ValueCountFrequency (%)
1035.4 1
< 0.1%
1032.4 1
< 0.1%
1032.2 1
< 0.1%
1032 1
< 0.1%
1031.4 1
< 0.1%
1031.2 1
< 0.1%
1030.8 1
< 0.1%
1030.5 1
< 0.1%
1030.1 2
< 0.1%
1030 1
< 0.1%
2023-03-16T14:08:16.367315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

presMax
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct444
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1015.8969
Minimum986.8
Maximum1038
Zeros0
Zeros (%)0.0%
Memory size94.8 KiB
2023-03-16T14:08:16.631916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum986.8
5-th percentile1008.3
Q11012.3
median1015
Q31019.2
95-th percentile1026.2
Maximum1038
Range51.2
Interquartile range (IQR)6.9

Descriptive statistics

Standard deviation5.5107898
Coefficient of variation (CV)0.0054245563
Kurtosis0.55943315
Mean1015.8969
Median Absolute Deviation (MAD)3.2
Skewness0.37263653
Sum12316734
Variance30.368805
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.221566777 × 10-23
2023-03-16T14:08:16.950065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1013.8 164
 
1.4%
1012.5 144
 
1.2%
1013.4 143
 
1.2%
1012.9 142
 
1.2%
1014.5 139
 
1.1%
1012.1 138
 
1.1%
1014.9 137
 
1.1%
1013.7 136
 
1.1%
1014.6 133
 
1.1%
1015 128
 
1.1%
Other values (434) 10720
88.4%
ValueCountFrequency (%)
986.8 1
< 0.1%
990.1 1
< 0.1%
991.8 2
< 0.1%
992.2 1
< 0.1%
993 1
< 0.1%
993.7 1
< 0.1%
994.4 1
< 0.1%
995.3 1
< 0.1%
995.7 1
< 0.1%
996 1
< 0.1%
ValueCountFrequency (%)
1038 1
< 0.1%
1035.8 1
< 0.1%
1035.6 1
< 0.1%
1035.4 1
< 0.1%
1035.1 2
< 0.1%
1035 1
< 0.1%
1034.8 1
< 0.1%
1034.5 1
< 0.1%
1034.4 1
< 0.1%
1034.3 1
< 0.1%
2023-03-16T14:08:17.641700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

dir
Real number (ℝ)

Distinct216
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.847668
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size94.8 KiB
2023-03-16T14:08:17.929927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q19
median22
Q327
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)18

Descriptive statistics

Standard deviation28.19449
Coefficient of variation (CV)0.97735766
Kurtosis1.9935538
Mean28.847668
Median Absolute Deviation (MAD)8
Skewness1.8009297
Sum349749.13
Variance794.92924
MonotonicityNot monotonic
2023-03-16T14:08:18.082489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 1538
 
12.7%
22 1346
 
11.1%
23 890
 
7.3%
24 822
 
6.8%
7 699
 
5.8%
21 608
 
5.0%
6 589
 
4.9%
25 540
 
4.5%
5 420
 
3.5%
27 405
 
3.3%
Other values (206) 4267
35.2%
ValueCountFrequency (%)
1 91
 
0.8%
2 147
 
1.2%
3 226
1.9%
4 273
2.3%
4.4 1
 
< 0.1%
4.83 1
 
< 0.1%
5 420
3.5%
5.14 1
 
< 0.1%
5.38 1
 
< 0.1%
5.56 1
 
< 0.1%
ValueCountFrequency (%)
99 1538
12.7%
73.33 1
 
< 0.1%
66.71 1
 
< 0.1%
64.22 1
 
< 0.1%
64.15 1
 
< 0.1%
63.71 1
 
< 0.1%
60.5 4
 
< 0.1%
60.25 1
 
< 0.1%
58.62 1
 
< 0.1%
57.83 1
 
< 0.1%

velmedia
Real number (ℝ)

Distinct85
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0058562
Minimum0
Maximum12.8
Zeros55
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size94.8 KiB
2023-03-16T14:08:18.232119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8
Q11.9
median2.8
Q33.9
95-th percentile5.8
Maximum12.8
Range12.8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5750283
Coefficient of variation (CV)0.52398658
Kurtosis0.81937715
Mean3.0058562
Median Absolute Deviation (MAD)1.1
Skewness0.73193511
Sum36443
Variance2.4807141
MonotonicityNot monotonic
2023-03-16T14:08:18.396649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 881
 
7.3%
3.1 870
 
7.2%
3.3 851
 
7.0%
1.9 812
 
6.7%
2.5 808
 
6.7%
1.7 788
 
6.5%
2.2 778
 
6.4%
3.9 672
 
5.5%
3.6 646
 
5.3%
1.4 646
 
5.3%
Other values (75) 4372
36.1%
ValueCountFrequency (%)
0 55
 
0.5%
0.3 152
 
1.3%
0.6 251
 
2.1%
0.8 457
3.8%
1.1 615
5.1%
1.4 646
5.3%
1.7 788
6.5%
1.9 812
6.7%
2.05 1
 
< 0.1%
2.06 1
 
< 0.1%
ValueCountFrequency (%)
12.8 1
 
< 0.1%
11.4 2
 
< 0.1%
10.8 1
 
< 0.1%
10.6 1
 
< 0.1%
10.3 3
 
< 0.1%
10 1
 
< 0.1%
9.7 4
< 0.1%
9.4 6
< 0.1%
9.2 9
0.1%
8.9 6
< 0.1%

racha
Real number (ℝ)

Distinct242
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7524084
Minimum1.9
Maximum31.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size94.8 KiB
2023-03-16T14:08:18.556224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile5.3
Q17.8
median9.4
Q311.4
95-th percentile15.6
Maximum31.9
Range30
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation3.1798518
Coefficient of variation (CV)0.3260581
Kurtosis1.97418
Mean9.7524084
Median Absolute Deviation (MAD)2
Skewness0.89985243
Sum118238.2
Variance10.111457
MonotonicityNot monotonic
2023-03-16T14:08:18.699839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.2 820
 
6.8%
10.3 818
 
6.7%
9.7 816
 
6.7%
8.3 773
 
6.4%
7.2 659
 
5.4%
11.4 638
 
5.3%
10.8 610
 
5.0%
8.9 550
 
4.5%
6.1 524
 
4.3%
7.8 519
 
4.3%
Other values (232) 5397
44.5%
ValueCountFrequency (%)
1.9 1
 
< 0.1%
2.5 2
 
< 0.1%
3.1 26
 
0.2%
3.6 66
 
0.5%
3.9 11
 
0.1%
4.2 111
0.9%
4.4 9
 
0.1%
4.7 183
1.5%
5 147
1.2%
5.3 210
1.7%
ValueCountFrequency (%)
31.9 1
 
< 0.1%
28.9 2
 
< 0.1%
27.8 1
 
< 0.1%
27.2 1
 
< 0.1%
26.7 1
 
< 0.1%
26.4 2
 
< 0.1%
25.8 2
 
< 0.1%
25.3 2
 
< 0.1%
24.7 6
< 0.1%
24.2 1
 
< 0.1%

sol
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct251
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4591834
Minimum0
Maximum14.4
Zeros368
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size94.8 KiB
2023-03-16T14:08:18.860411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q16.3
median9.3
Q311.4
95-th percentile13.1
Maximum14.4
Range14.4
Interquartile range (IQR)5.1

Descriptive statistics

Standard deviation3.7484994
Coefficient of variation (CV)0.4431278
Kurtosis-0.32157337
Mean8.4591834
Median Absolute Deviation (MAD)2.4
Skewness-0.7569277
Sum102559.14
Variance14.051247
MonotonicityNot monotonic
2023-03-16T14:08:19.012005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 368
 
3.0%
9.8 218
 
1.8%
9.5 216
 
1.8%
9.3 210
 
1.7%
9.2 210
 
1.7%
9.4 208
 
1.7%
12.6 203
 
1.7%
9 198
 
1.6%
10 191
 
1.6%
9.7 185
 
1.5%
Other values (241) 9917
81.8%
ValueCountFrequency (%)
0 368
3.0%
0.1 54
 
0.4%
0.2 62
 
0.5%
0.3 55
 
0.5%
0.4 42
 
0.3%
0.5 37
 
0.3%
0.6 44
 
0.4%
0.7 55
 
0.5%
0.8 46
 
0.4%
0.9 33
 
0.3%
ValueCountFrequency (%)
14.4 2
 
< 0.1%
14.3 25
0.2%
14.2 31
0.3%
14.1 41
0.3%
14 38
0.3%
13.9 35
0.3%
13.8 52
0.4%
13.7 50
0.4%
13.6 50
0.4%
13.5 51
0.4%

prec
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct530
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4175396
Minimum0
Maximum109.3
Zeros9526
Zeros (%)78.6%
Negative0
Negative (%)0.0%
Memory size94.8 KiB
2023-03-16T14:08:19.175569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8.9
Maximum109.3
Range109.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.1994972
Coefficient of variation (CV)3.6679732
Kurtosis67.301784
Mean1.4175396
Median Absolute Deviation (MAD)0
Skewness6.6333771
Sum17186.25
Variance27.034771
MonotonicityNot monotonic
2023-03-16T14:08:19.325169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9526
78.6%
0.1 158
 
1.3%
0.2 118
 
1.0%
0.3 81
 
0.7%
0.4 60
 
0.5%
0.5 50
 
0.4%
0.6 49
 
0.4%
1 47
 
0.4%
0.7 43
 
0.4%
0.8 41
 
0.3%
Other values (520) 1951
 
16.1%
ValueCountFrequency (%)
0 9526
78.6%
0.02 11
 
0.1%
0.03 1
 
< 0.1%
0.04 4
 
< 0.1%
0.05 4
 
< 0.1%
0.06 4
 
< 0.1%
0.07 3
 
< 0.1%
0.08 3
 
< 0.1%
0.1 158
 
1.3%
0.12 4
 
< 0.1%
ValueCountFrequency (%)
109.3 1
< 0.1%
106.1 1
< 0.1%
80.3 1
< 0.1%
72.5 1
< 0.1%
69.5 1
< 0.1%
67.3 1
< 0.1%
63.4 1
< 0.1%
63.2 1
< 0.1%
60.6 1
< 0.1%
54.5 1
< 0.1%

Interactions

2023-03-16T14:08:09.864578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:57.758976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:59.150744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:00.489044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:01.821354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:03.115419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:04.524673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:05.806247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:07.089817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:08.355407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:09.998222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:57.906702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:59.282694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:00.623268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:01.959549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:03.253072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:04.654326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:05.943880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:07.221465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:08.497029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:10.110920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:58.044034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:59.392715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:00.744982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:02.076264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:03.376740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:04.767025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:06.059570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:07.335163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:08.621696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:10.239577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:58.216649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:59.518106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:00.881315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:02.207680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:03.507391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:04.908646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:06.192228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:07.464814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:08.889979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:10.364243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:58.356373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:59.642746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:01.012101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:02.335467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:03.632059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:05.038302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:06.323867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:07.593472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:09.024626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:10.494895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:58.490982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:59.764067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:01.143393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:02.466624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:03.755728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:05.164961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:06.456509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:07.720135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:09.157263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:10.627540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:58.620362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:59.883382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:01.277001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:02.590474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:03.882418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:05.291623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:06.578183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:07.842804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:09.291906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:10.758218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:58.754551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:00.008511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:01.414654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:02.722090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:04.014038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:05.423272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:06.705843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:07.977417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:09.427547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:10.878868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:58.884282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:00.130307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:01.548037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:02.849380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:04.144688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:05.555916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:06.828515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:08.099091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:09.575148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:11.009519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:07:59.022041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:00.259019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:01.690536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:02.987988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:04.394021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:05.686567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:06.965150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:08.230772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T14:08:09.736920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-16T14:08:19.456846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
tmintmaxtmedpresMinpresMaxdirvelmediarachasolprec
tmin1.0000.8840.963-0.426-0.5090.1770.1980.2230.426-0.155
tmax0.8841.0000.976-0.273-0.3580.1430.0730.1100.686-0.393
tmed0.9630.9761.000-0.355-0.4400.1640.1340.1680.585-0.291
presMin-0.426-0.273-0.3551.0000.933-0.200-0.315-0.419-0.027-0.312
presMax-0.509-0.358-0.4400.9331.000-0.196-0.287-0.376-0.080-0.250
dir0.1770.1430.164-0.200-0.1961.0000.0470.0430.0650.050
velmedia0.1980.0730.134-0.315-0.2870.0471.0000.7830.0200.154
racha0.2230.1100.168-0.419-0.3760.0430.7831.0000.0250.239
sol0.4260.6860.585-0.027-0.0800.0650.0200.0251.000-0.570
prec-0.155-0.393-0.291-0.312-0.2500.0500.1540.239-0.5701.000

Missing values

2023-03-16T14:08:11.184051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-16T14:08:11.394489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

tmintmaxtmedpresMinpresMaxdirvelmediarachasolprec
010.016.213.11016.21019.320.02.55.31.215.6
17.614.611.11010.61019.320.01.715.60.826.1
27.613.010.31008.11014.418.02.29.70.36.4
34.414.89.61014.41023.331.02.27.24.90.0
46.415.811.11023.11026.46.02.26.16.70.0
56.816.611.71022.61025.17.03.38.35.30.0
67.617.612.61024.61027.431.00.35.65.60.0
77.216.211.71024.91027.77.06.114.49.30.0
87.416.612.01025.91029.16.05.010.88.30.0
94.915.510.21022.21026.36.02.29.29.10.0
tmintmaxtmedpresMinpresMaxdirvelmediarachasolprec
121140.118.59.31013.91018.15.01.45.810.80.0
121156.817.912.41014.51017.699.01.95.05.20.0
121166.015.510.81011.01015.399.00.65.00.01.5
1211710.315.913.11008.71013.421.01.98.30.03.3
1211812.919.716.31009.81013.422.03.910.33.20.6
1211914.420.517.41011.61015.122.06.110.33.22.5
1212013.620.517.01013.71019.521.05.312.53.00.9
121218.921.815.41019.21022.599.01.76.710.50.1
121228.924.016.41018.71022.299.02.87.89.90.0
121239.526.518.01014.11020.38.02.28.310.60.0